Evolution of Controllability in Interbank Networks

Abstract

The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected “hub” institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies.

Network Controllability Is Determined by the Density of Low In-Degree and Out-Degree Nodes

“…The controllability of a network [1][2][3][4][5][6][7][8][9][10] is a fundamental problem with wide applications ranging from medicine and drug discovery [11], to the characterization of dynamical processes in the brain [12][13][14], or the evaluation of risk in financial markets [15]. While the interplay between the structure of the network [16][17][18][19] and the dynamical processes defined on them has been an active subject of complex network research for more than ten years [20,21], only recently the rich interplay between the controllability of a network and its structure has started to be investigated.…”

Realizing actual feedback control of complex network

“…Many relevant papers emerge [13][14][15][16][17][18][19][20][21][22] after the seminal work of controllability of complex networks [12], but the problem of feedback control of complex networks is not proposed and solved strictly and completely. In this paper, we show a framework to study the feedback control of an arbitrary complex directed network.…”

Risk analysis and controllability of credit market

“…Knowing the characteristics of such networks, questions arise naturally about how to control such financial systems. This controllability aspect will give key tools to regulators, financial actors and policy makers to anticipate all necessary measures to avoid risk and systemic collapse of the whole system [4].…”

“…It gives new insights on how to control financial systems [4,6]. Nevertheless, it is important to specify the underlying dynamics behind each system.…”